Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

Astrid Vannoppen,Leonid Bobylev,Bert Van Schaeybroeck,Svetlana Aniskevich,Lola Kotova,Hans Van De Vyver,Andris Vīksna,Piet Termonia,Natalia Gnatiuk,Sara Top,Abdulla Sakalli,Rafiq Hamdi,Lesley De Cruz,Armelle Reca Remedio,Anne Gobin,Steven Caluwaerts,Rozemien De Troch,Lars Buntemeyer

doi:10.3390/rs12142206

Abstract

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

Highlights

Extreme weather events and long-term climate change effects will have a large impact on the yield of agriculture crops in the future [1,2,3,4]
For both winter and spring wheat, the Normalized Difference Vegetation Index (NDVI) dropped around the end of August which coincided with the harvest time of winter and spring wheat in Latvia
Wheat yield was estimated from NDVI time series for spring and winter wheat in Latvia

Summary

Introduction

Extreme weather events and long-term climate change effects will have a large impact on the yield of agriculture crops in the future [1,2,3,4]. A negative impact on major food and feed crops is expected in temperate and tropical regions under the climate change scenario of global warming equal to 2 ◦C or more compared to the pre-industrial era [4]. The magnitude of the CO2 fertilization effect and the importance of other interacting factors is still unclear [4,5]. Considering these scenarios, it will be a challenge to ensure global food security in the future. Years with extreme heat waves between 1964 and 2007 resulted in national cereal production deficits of 9.1% worldwide [8]

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Conclusion